Performance Charts Creation
Last updated
Last updated
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To create a Performance chart, follow these steps:
Navigate to the Charts
tab in your Fiddler AI instance
Click on the Add Chart
button on the top right
In the modal, Select the project that has a model with Custom features
Select Performance Analytics
Once you're satisfied with your visualization, you can save the chart. This chart can then be added to a Dashboard. This allows you to revisit the Performance visualization at any time easily either directly going to the Chart or to the dashboard.
Model task | Available Chart(s) |
---|---|
Parameter | Value |
---|---|
Control | Model Task | Value |
---|---|---|
Binary classification
- Confusion Matrix - ROC - Precision Recall - Calibration Plot Charts
Multi-class Classification
- Confusion Matrix
Regression
- Prediction Scatterplot - Error Distribution
Ranking / LLM / Not Set
Not available
Model
List of models in the project
Version
List of versions for the selected model
Environment
Production
or Pre-Production
Dataset
Displayed only if Pre-Production
is selected. List of pre-production env uploaded for the model version.
Visual
List of possible performance visualization depending on the model task.
Segment
- Selecting a saved segment - Defining an applied (on the fly) segment. This applied segment isnβt saved (unless specifically required by the user) and is applied for analysis purposes.
Max Sample size
Integer representing the maximum number of rows used for computing the chart, up to 500,000. If the data selected has less rows, we will use all the available rows with non null target and output(s).
Fiddler select the n
first number of rows from the selected slice.
Note: Clickhouse is configured using multiple shards, which means slightly different results can be observed if data is only selected on a specific shard (usually when little observation are queried).
Time range selection
All
Selecting start time and end time or time label for production data. Default to last 30 days
Positive class threshold
Binary classification
Selecting threshold applied for computation / visualization. Default to 0.5
Displayed labels
Multi-class Classification
Selecting the labels to display on the confusion matrix (up to 12). Default to the 12 first labels